PROJECT SUMMARY. Regulation of gene expression is of paramount importance in animal development, with improper regulation resulting in developmental defects and disease states. At the DNA level, gene regula- tion can be achieved by transcription factors binding to their cognate sequences, which are often clustered to- gether. In developing tissues, several genes coding for transcription factors regulate each other in a complex web of interactions known as the genetic regulatory network (GRN). The structure of a GRN is thought to be responsible for the robust and precise cell fate decisions required in a developing tissue. However, there re- main unknown components participating in the native GRN, limiting a full understanding of how the structure of the GRN results in robust cell fate decisions. The long-term goal is to deduce the genetic regulatory interactions necessary for robust patterns of gene ex- pression. The overall objective in this proposal is to use the natural variation that occurs in a panel of wild- caught fly lines to characterize the GRN responsible for precise anterior-posterior (AP) patterning the early Drosophila embryo. This will test the central hypothesis that gene expression patterns in the AP patterning sys- tem have undiscovered regulation that may explain the robustness of gene expression, and can be found by examining the correlation between gene expression and natural variation in the genomes of these flies. Specific Aim 1: Use natural variation to correlate DNA elements to gene expression patterns. Based on our preliminary data, our working hypothesis is that novel DNA elements --- outside of standard, well- characterized enhancers --- exert control on the expression patterns of AP network genes. To test this hypoth- esis, we will measure gene expression patterns in DGRP lines and correlate the measurements to genomic sequences. If successful, our work in this Aim will result in discovery of novel DNA elements, which would ad- vance our understanding of general mechanisms of gene regulation. Specific Aim 2: Use natural variation to correlate DNA elements to transcriptomic regulation. In complement to the previous aim, the goal in this aim is to correlate global transcriptomic data to natural genomic variation in order to discover novel AP pattern- ing targets. Next-Gen sequencing will be used to generate large transcriptomic data sets for discovery. Spe- cific Aim 3: Build a comprehensive model of the AP patterning network. The goal of this Aim is to synthe- size large-scale data from the literature and from DGRP lines to build a comprehensive model of the AP pat- terning network. If successful, our work in this Aim will result in model-generated, testable predictions regard- ing robustness of gene expression and advance our understanding of GRNs at a quantitative level. The following outcomes are expected: First, novel regulation of known AP components will be discovered. Conversely, previously unknown components of the AP patterning network will be discovered. Moreover, this work will lead to a quantitative understanding of GRN behavior.